title: GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA.
abstract: This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.
authors.forename: Nicole
authors.initials: N
authors.lastname: Labra
authors.forename: Miguel
authors.initials: M
authors.lastname: Figueroa
authors.forename: Pamela
authors.initials: P
authors.lastname: Guevara
authors.forename: Delphine
authors.initials: D
authors.lastname: Duclap
authors.forename: Josselin
authors.initials: J
authors.lastname: Hoeunou
authors.forename: Cyril
authors.initials: C
authors.lastname: Poupon
authors.forename: Jean-Francois
authors.initials: JF
authors.lastname: Mangin
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D016208:Databases, Factual; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D011859:Radiography; D012984:Software; D066127:White Matter / Q000000981:diagnostic imaging